What you'll learn

As agentic AI applications that interact with large language models move from prototype to production, the gap between a demo that impresses in a notebook and a system that performs reliably at enterprise scale is almost always a context problem. In this course, Context Optimization and Orchestration, you will learn the skills of a practitioner who can design, deploy, debug, and continuously improve the retrieval and orchestration infrastructure that determines what an AI system knows at the moment it needs to respond.

First, you will discover how to design and implement context pipelines that make intelligent, metadata-driven decisions about which retriever to invoke for a given query.

Next, you will explore hands-on proficiency with LangGraph modeling, multi-agent context workflows as stateful directed graphs.

Finally, you will learn how to close the loop with automated evaluation pipelines that detect metric regressions, trigger retrieval parameter sweeps, and flag low-confidence runs for human review that transform your observability stack from a passive logging system into an active quality assurance engine.

When you’re finished with this course, you’ll have the skills and knowledge of context optimization and orchestration needed to design, deploy, debug, and continuously improve the retrieval and orchestration infrastructure that determines what an AI system knows at the moment it needs to respond.